SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2776–2800 of 7282 papers

TitleStatusHype
Finding Local Diffusion Schrodinger Bridge using Kolmogorov-Arnold NetworkCode0
DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative DenoisingCode0
Fine-grained Contrastive Learning for Relation ExtractionCode0
Credit Card Fraud Detection Using Autoencoder Neural NetworkCode0
DiffuScene: Denoising Diffusion Models for Generative Indoor Scene SynthesisCode0
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic ModelCode0
On the Importance of Denoising when Learning to Compress ImagesCode0
CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion ModelsCode0
Fine-grained Forecasting Models Via Gaussian Process Blurring EffectCode0
First line of defense: A robust first layer mitigates adversarial attacksCode0
Few-shot point cloud reconstruction and denoising via learned Guassian splats renderings and fine-tuned diffusion featuresCode0
Few-shot Image Generation with Diffusion ModelsCode0
Accurate Segmentation of Optic Disc And Cup from Multiple Pseudo-labels by Noise-aware LearningCode0
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
FFDNet: Toward a Fast and Flexible Solution for CNN based Image DenoisingCode0
An Extended Framework for Marginalized Domain AdaptationCode0
Few Clean Instances Help Denoising Distant SupervisionCode0
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
Feature-Based Image Clustering and Segmentation Using WaveletsCode0
Feature Enhancement with Deep Feature Losses for Speaker VerificationCode0
Face Manifold: Manifold Learning for Synthetic Face GenerationCode0
Face Morphing Attack Detection with Denoising Diffusion Probabilistic ModelsCode0
Active Generation for Image ClassificationCode0
A denoised Mean Teacher for domain adaptive point cloud registrationCode0
FDF: Flexible Decoupled Framework for Time Series Forecasting with Conditional Denoising and Polynomial ModelingCode0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified